A Comparative Systematic Review of COVID-19 and Influenza

Review A Comparative Systematic Review of COVID-19 and Inﬂuenza

Molka Osman 1 , Timothée Klopfenstein 2, Nabil Belfeki 3, Vincent Gendrin 2,* and Souheil Zayet 2,*

1 Faculty of Medicine of Tunis, University Tunis El Manar, Tunis 1007, Tunisia; [email protected] 2 Infectious Disease Department, Nord Franche-Comté Hospital, 90400 Trévenans, France; [email protected] 3 Internal Medicine Department, Groupe Hospitalier Sud Ile de France, 77000 Melun, France; [email protected] * Correspondence: [email protected] (V.G.); [email protected] (S.Z.)

Abstract: Background: Both SARS-CoV-2 and influenza virus share similarities such as clinical features and outcome, laboratory, and radiological findings. Methods: Literature search was done using PubMed to find MEDLINE indexed articles relevant to this study. As of 25 November 2020, the search has been conducted by combining the MeSH words “COVID-19” and “Influenza”. Results: Eighteen articles were finally selected in adult patients. Comorbidities such as cardiovascular diseases, diabetes, and obesity were significantly higher in COVID-19 patients, while pulmonary diseases and immunocompromised conditions were significantly more common in influenza patients. The incidence rates of fever, vomiting, ocular and otorhinolaryngological symptoms were found to be significantly higher in influenza patients when compared with COVID-19 patients. However, neurologic symptoms and diarrhea were statistically more frequent in COVID-19 patients. The level of white cell count and procalcitonin was significantly higher in influenza patients, whereas thrombopenia and elevated transaminases were significantly more common in COVID-19 patients. Ground-grass opacities, interlobular septal thickening, and a peripheral distribution were more Citation: Osman, M.; Klopfenstein, common in COVID-19 patients than in influenza patients where consolidations and linear opacities T.; Belfeki, N.; Gendrin, V.; Zayet, S. A were described instead. COVID-19 patients were significantly more often transferred to intensive Comparative Systematic Review of care unit with a higher rate of mortality. Conclusions: This study estimated differences of COVID-19 COVID-19 and Influenza. Viruses and influenza patients which can help clinicians during the co-circulation of the two viruses. 2021, 13, 452. https://doi.org/ 10.3390/v13030452 Keywords: influenza; COVID-19; SARS-CoV-2; clinical features; laboratory; imaging; systematic review

Academic Editor: Luis Martinez-Sobrido

Received: 14 February 2021 1. Introduction Accepted: 8 March 2021 Coronavirus disease 2019 (COVID-19), an infection caused by severe acute respiratory Published: 10 March 2021 syndrome coronavirus-2 (SARS-CoV-2), was first reported in December 2019, in Wuhan, province of Hubei in China. Since then, it has evolved into a worldwide pandemic and Publisher’s Note: MDPI stays neutral became one of the world’s toughest health problems [1]. Both SARS-CoV-2 and influenza with regard to jurisdictional claims in virus share similarities such as viral shedding, route of transmission, and clinical presen- published maps and institutional affil- tation [2]. Influenza affects 5–10% of adults annually with most cases occurring during iations. the winter season [3]. According to the World Health Organization (WHO), influenza’s epidemics are estimated to result in about 3 to 5 million cases of severe illness, and about 290,000 to 650,000 respiratory deaths [4]. There have been few studies comparing COVID-19 with influenza mainly focused Copyright: © 2021 by the authors. on epidemiological features in the general population [5]. However, no review study Licensee MDPI, Basel, Switzerland. compared clinical, biological, and radiological characteristics between COVID-19 and This article is an open access article influenza. With projections estimating the COVID-19 pandemic to last for another year [2] distributed under the terms and and it being the influenza season, it is crucial to evaluate the differences between the two conditions of the Creative Commons diseases. The current study performed a systematic review to compare the clinical features Attribution (CC BY) license (https:// and outcome, laboratory, and radiological findings of COVID-19 patients with influenza creativecommons.org/licenses/by/ adult patients. 4.0/).

Viruses 2021, 13, 452. https://doi.org/10.3390/v13030452 https://www.mdpi.com/journal/viruses Viruses 2021, 13, 452 2 of 14

2. Methods This study is based on a review of published literature comparing COVID-19 and influenza. Literature search was done using PubMed to find MEDLINE indexed articles relevant to this study. As of 25 November 2020, the search has been conducted by combining the MeSH words “COVID-19” and “Influenza”. At first, citations that were not in English or in French and not a free full text were excluded. Then, we screened citations based on titles and abstracts. Those with irrelevant information or subjects, studies with focus other than comparing COVID-19 and influenza, studies with a population under 18 years old and case reports about coinfection between COVID-19 and influenza were excluded. A p-value < 0.05 was considered significant to compare the two viruses.

3. Results 3.1. Included Citations in the COVID-19 vs. Inﬂuenza Review On PubMed, 331 articles were initially found, out of which 46 articles were selected. In the end, 18 articles were selected [3,5–21] (8 retrospective studies, 6 cohort studies, 3 case- control studies, 1 prospective study, 1 cross-sectional study, 1 review and meta-analysis, and 1 review) (Figure1 and Table1)

Figure 1. PRISMA guideline ﬂowchart detailing article selection process. Viruses 2021, 13, 452 3 of 14

Table 1. General summary of included citations.

Number of Patients/Studies * References Country Study Type ** (COVID-19 vs. Influenza) Jordan Cates et al. [6] United States Cohort 9401 (3948 vs. 5453) Ying Luo et al. [7] China (Hubei) Cohort 2167 (1027 vs. 1140) Jiangnan Chen et al. [8] China (Shaoxing) Case-Control 380 (169 vs. 131; 80 healthy controls) Jiajia Qu et al. [9] China Retrospective Cohort 366 (246 vs. 120) Jianguo Zhang et al. [10] China Retrospective cohort 326 (211 vs. 115) Helene Faury et al. [11] France (Paris) Retrospective 200 (100 vs. 100) Systematic review and Pengfei Li et al. * [5]- 197 (113 vs. 84) Meta-analysis Mengqi Liu et al. [12] China (Chongqing) Retrospective 180 (122 vs. 48) Xiao Tang et al. [13] China (Wuhan) Retrospective case-control 148 (73 vs. 75) Natalie L. Cobb et al. [14] United States (Washington) Retrospective Cohort 139 (65 vs. 74) Souheil Zayet et al. [15] France Retrospective 124 (70 vs. 54) Hao Wang et al. [16] China Retrospective 105 (13 vs. 92) Liaoyi Lin et al. [17] China (Wenzhou) Retrospective 97 (52 vs. 45) Raija Auvinen et al. [18] Finland Prospective study 61 (28 vs. 33) Zhilan Yin et al. [19] China Retrospective 60 (30 vs. 30) A cross-sectional Yi-Hua Lin et al. [20] China (Xiamen) 57 (35 vs. 22) retrospective study Jaehee Lee et al. [21] South Korea (Daegu) Retrospective 29 (20 vs. 09) Stephen O. Onigbinde et al. * [3] - Review 17 (09 vs. 08) * Studies for Review or Meta-analysis. ** Cohort studies are used to investigate causes of disease and establish association between risk factors and health outcomes. An outcome-free study population is first identified by the exposure/event of interest and followed in time until the outcome of interest happens. They can be prospective (carried out from the present time into the future) or retrospective (carried out at the present time and look to the past to examine medical events or outcome). Case-Control studies first identify subjects by outcome status (cases), then select from the same source population, subjects without the outcome (control). Cross-sectional study or prevalence study is an observational study that collects data on the subjects of interest at a specific point in time.

3.2. Studies in Patients with COVID-19 and Influenza 3.2.1. Demographic and Clinical Findings In this review, 14 studies of 18 [5,6,9–16,18–21] reported a comparison between COVID- 19 and influenza patients based on demographic findings, comorbidities, clinical features, and outcome (Table2). Fever and respiratory symptoms such as cough, expectoration or sputum production and dyspnea were the main symptoms in both groups with COVID-19 and influenza; however, they were significantly more frequent in patients with influenza [5,9–11,13,15,16,19,20]. Moreover, vomiting, otorhinolaryngological symptoms such as nasal congestion, rhinorrhea, sore throat and ocular symptoms such as tearing and conjunctival hyperhemia were statistically more frequent in patients with influenza than COVID-19 adult patients [5,10–12,15]. In patients infected with COVID-19, the most significant and frequent clinical features were fatigue, neurologic symptoms such as headache (especially facial headache: retro-orbital or frontal headache), anosmia and dysgeusia, gastro-intestinal (GI) symptoms such as diarrhea and acute respiratory distress syndrome (ARDS), compared to influenza patients [11,13–15,18]. In a retrospective case-control study, Tang et al. reported that patients with influenza were more disposed to have productive cough and higher Sequential Organ Failure Assessment (SOFA) scores (OR = 9.58,(95% CI = 1.73–64.72), p = 0.011 and OR = 2.26 (1.12–3.57), p = 0.006, respectively). Meanwhile, COVID-19 patients exhibit symptoms of fatigue or GI symptoms (OR = 0.91 (0.84–0.98), p = 0.011; OR = 0.12 (0.02–0.94), p = 0.013 and OR = 0.10, (0.01–0.98), p = 0.044, respectively) [13]. Viruses 2021, 13, 452 4 of 14

Table 2. Signiﬁcant demographic and baseline characteristics, clinical features, and outcome in COVID-19 and inﬂuenza adult groups.

References Signiﬁcant Clinical Features/Outcome COVID-19 (%) Inﬂuenza (%) p-Value < 0.05 Admitted to ICU 36.5 17.6 <0.001 Jordan Cates et al. [6] Hospital mortality 21 3.8 <0.001 Duration of hospitalization 8.6 [3.9–18.6] 3.0 [1.8–6.5] <0.001 (median days, [IQR]) Fever 78.5 89.2 <0.05 Jiajia Qu et al. [9] Persistent fever 50.4 74.2 <0.01 Cough 69.7 86.1 0.001 Expectoration 22.7 74.8 <0.001 Jianguo Zhang et al. [10] Dyspnea 14.7 27.8 0.004 Chest pain 13.7 27.8 0.002 Vomiting 1.4 9.6 <0.001 Chronic pulmonary diseases 12.0 27.0 0.01 Overweight/Obesity 40.8 25.0 0.02 Median BMI 27.3 24.8 0.04 Fatigue 63.6 39.0 0.0006 Diarrhea 25.8 13.0 0.03 Faintness 12.1 3.0 0.02 Anosmia/Ageusia 7.0 0 0.01 Sputum production 12.0 36.0 0.0001 Helene Faury et al. [11] Nasal Congestion 8.3 21 0.02 Secondary respiratory failure 21.0 0 <0.0001 Acute Kidney failure 17.0 7.0 0.048 Pulmonary embolism 6.0 0 0.03 Heart congestion 2.0 14.0 0.003 Admitted to ICU 31.0 12.0 0.002 Duration of hospitalization (days, [IQR]) 10 [4–17] 4 [1–11] <0.0001 Oxygen therapy 65.0 42.3 0.002 Mortality rate 20.0 5.0 0.002 Cardiovascular disease/Hypertension 28.76 14.11 <0.0001 Diabetes 16.38 11.12 0.012 Asthma 8.42 16.09 0.0033 Chronic Obstructive Pulmonary disease 4.93 9.52 0.0003 Immunocompromised conditions 4.39 9.99 <0.0001 Fever 72.08 89.99 <0.0001 Pengfei Li et al. [5] Cough 57.99 85.31 <0.0001 Shortness of breath 32.89 49.19 0.0249 Rhinorrhea 8.48 38.57 <0.0001 Sore throat 9.48 37.28 <0.0001 Myalgia/Muscle pain 18.97 30.12 0.0242 Vomiting 8.67 24.27 <0.0001 Mengqi Liu et al. [12] Stuffy and runny nose 7 23 0.002 Productive cough 53.4 78.7 0.002 Fatigue 63 18.7 <0.001 Xiao Tang et al. [13] GI symptoms 37 6.7 <0.001 Myalgia 34.2 14.7 0.007 ARDS 63 26 <0.001 Natalie L. Cobb et al. [14] Hospital mortality 40 19 0.006 Viruses 2021, 13, 452 5 of 14

Table 2. Cont.

References Significant Clinical Features/Outcome COVID-19 (%) Influenza (%) p-Value < 0.05 Frontal headache 25.7 9.3 0.021 Retro-orbital or temporal headache 18.6 3.7 0.013 Fever 75.7 92.6 0.042 Anosmia 52.9 16.7 <0.001 Dysgeusia 48.6 20.4 0.001 Diarrhea 40 20.4 0.021 Sputum Production 28.6 51.9 0.01 Souheil Zayet et al. [15] Sneezing 18.6 46.3 0.001 Dyspnea 34.3 59.3 0.007 Sore throat 20 44.4 0.006 Conjunctival hyperemia 4.3 29.6 <0.001 Tearing 5.7 24.1 0.004 Vomiting 2.8 22.2 0.001 Crackling sound 38.6 20.4 0.032 Ronchi sounds 1.4 16.7 0.002 Hao Wang et al. [16] Cough 30.8 82.6 0 Pulmonary Diseases 18 45 0.03 Current smoking 4 30 0.008 Headache 85 52 0.004 Raija Auvinen et al. [18] ARDS 93 58 0.003 ICU admission 29 6 0.034 Duration of hospitalization (days, [IQR]) 6 [4–21] 3 [2–4] <0.001 Cough 73.3 96.7 0.026 Zhilan Yin et al. [19] Expectoration 43.3 80 0.007 Fever 38.0 ◦C–38.9 ◦C 43 32 0.014 Fever ≥39.0 ◦C 11 45 0.014 Cough 51 100 <0.001 Yi-Hua Lin et al. [20] Expectoration 28 91 <0.001 Dyspnea 9 59 <0.001 Chills 23 55 0.015 Jaehee Lee et al. [21] Median heart rate (bpm) 83 107 0.017 Bold: illness (COVID-19 or influenza) with significant difference. Abbreviations: ARDS: Acute respiratory distress syndrome; ICU: Intensive care unit; IQR: interquartile range.

Concerning comorbidities; cardiovascular disease/hypertension, diabetes and obesity were significantly higher in patients with COVID-19, while respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) and immunocompromised conditions were significantly more common in influenza patients [5,11,18]. Zayet et al. concluded that COVID-19 patients had a lower Charlson comorbidity index (CCI) than influenza patients (2 ± 2.5 vs. 3 ± 2.4, respectively, p = 0.003), but with no significant differences regarding comorbidities [15]. Qu et al. reported that while the incidences of COVID-19 and influenza were comparable among the 18–65 and >65 year age groups, the incidences of influenza were much higher than COVID-19 among those aged under 18 years old [9]. However, Lee et al., reported that the median age was significantly higher in patients with COVID-19 compared to patients with influenza (68 (IQR: 59–75) years vs. 57 (IQR: 44–63) years, p = 0.016) [21]. No significant difference was found concerning other demographic characteristics such as gender and ethnicity in our review. Only a few studies (2/18 studies in adults) were interested in describing the timeline and onset of the main symptoms in COVID-19 and influenza [14,15]. Zayet et al. reconsti- tuted the natural history of the two viruses and suggested that the onset of symptoms (pain symptoms appeared first, followed by fever, cough and diarrhea) did not differ between the two groups except fever, which appeared earlier in COVID-19 than in influenza (1.9 days vs. 2.5 days, p = 0.045) [15]. They also concluded that hospitalization and clinical aggravation occurred later in COVID-19 than in influenza. In fact, COVID-19 patients were hospitalized Viruses 2021, 13, 452 6 of 14

at day 7 ± 3 (vs. day 5 ± 2 in influenza, p = 0.038), had a respiratory rate ≥ 22/min at day 9 ± 0.8 (vs. day 5 ± 1.3 in influenza, p < 0.001), and were admitted to intensive care unit (ICU) at day 10 ± 2.7 (vs. day 7 ± 2.4 in influenza, p < 0.004). Cobb et al. also reported that patients with COVID-19 had a longer median symptom duration prior to hospitalization compared to patients with influenza (7 days (IQR 5–13) vs. 3.5 (IQR 2–7), p < 0.001) [14]. In the prospective study of Raija Auvinen et al., in multivariable Cox regression analysis, the predictors associated with a longer duration of hospitalization were COVID-19 (hazard ratio (HR) 0.221, (95% CI = 0.118–0.416), p < 0.001), age (HR = 0.972, (0.955–0.990), p = 0.002), Body Mass Index (HR = 0.950 (0.916–0.986), p = 0.006) and diabetes (HR 0.539, (0.272–1.066), p = 0.076) [18].

3.2.2. Laboratory Findings A total of 11 studies of 18 [7–11,13,14,18–21] compared routine laboratory test results between COVID-19 and influenza patients. Both infections caused abnormalities in biological parameters leading to aberrant blood cell counts and sometimes elevated hepatic, renal, and cardiac enzyme activity. White blood cells, especially neutrophils (NE) were significantly more elevated in influenza than COVID-19 patients [7,8,10,11,14,18–21]. Lymphocytes proportion in white blood cell count are higher in influenza than COVID-19 patients [7,8,20] but without difference between lymphocytes level [7,8], except for one study [20]. Platelets level was lower [11] with more often thrombocytopenia [18] in COVID- 19 than influenza patients. Elevated transaminases were significantly more common in COVID-19 patients [7,8,11,18,20]. In a prospective study by Raija Auvinen et al, it was reported that C-reactive protein (CRP) values were similar at admission but rose significantly higher in COVID-19 patients than influenza patients during hospitalization [18]. However, elevated procalcitonin was significantly more frequent in influenza patients than in COVID-19 patients [19,20]. COVID-19 patients had also a greater disposition to elevated prothrombin time (OR = 0.63, 95% CI (0.46–0.86), p = 0.004) [13]. Finally, Chen et al. developed a diagnostic formula to differentiate between COVID- 19 and influenza during their early stages. Two parameters (monocyte (MO) count and percentage of basophils (BA)) were combined to clarify the diagnostic efficacy, with a sensitivity of 71.6% and a specificity of 74.8% (joint probability (P) = 2.388 × BA% − 5.182 × MO# + 2.192 (AUC 0.772, 95% CI (0.718–0.826)). COVID-19 should be considered as the diagnosis when the joint probability is greater than 0.45, while influenza should be considered when it is less than 0.45 [8] (Table3).

3.2.3. Radiological Findings We found 10 studies of 18 on adult patients [10–14,16–20] that described imaging findings in COVID-19 and influenza, especially in chest computed tomography (CT) manifestations. Ground-grass opacities (GGO), interlobular septal thickening, and a peripheral distribution were more common in patients with COVID-19 than in patients with influenza [10,12,13,16,18,20]. However, consolidation, nodules, and linear opacities were more common in patients with influenza than those with COVID-19 [11–13,18,19]. Tang et al. reported that influenza patients were more inclined to have consolidation manifested on chest CT imaging (OR = 4.95 (95% CI = 1.518–16.176), p = 0.008), whereas COVID-19 patients had a greater disposition for having GGO on chest CT scans (OR = 0.086 (0.015–0.490); p = 0.006) [13]. In a review from Onigbinde et al., GGO were usually peripherally located and that vascular engorgement, pleural thickening, and subpleural lines were more frequent in COVID-19 patients. Pneumomediastinum and pneumothorax were only reported in influenza studies [3] (Table4). Viruses 2021, 13, 452 7 of 14

Table 3. Significant laboratory findings in COVID-19 and influenza adult groups.

References Signiﬁcant Laboratory Findings COVID-19 (%) Inﬂuenza (%) p-Value < 0.05 White blood cell count (×109 /L, 5.45 [4.46–7.17] 6.14 [4.66–8.24] <0.001 median, [IQR]) Neutrophil (×109 /L, median, [IQR])) 3.68 [2.68–5.16] 4.09 [2.85–6.11] <0.001 Lymphocyte (%, median, [IQR]) 22.0 [14.6–29.4] 20.5 [13.3–28.6] 0.009 Monocyte (×109 /L, median, [IQR]) 0.47 [0.34–0.61] 0.52 [0.37–0.69] <0.001 Eosinophil (×109 /L, median, [IQR]) 0.01 [0.00–0.05] 0.02 [0.00–0.07] <0.001 Eosinophil (%, median, [IQR]) 0.2 [0.0–2.9] 0.3 [0.0–1.2] <0.001 Basophil (%, median, [IQR]) 0.2 [0.0–0.3] 0.2 [0.1–0.3] <0.001 Red blood cell count (×1012 /L, 4.43 [4.00–4.84] 4.37 [3.96–4.78] 0.012 median, [IQR]) Hemoglobin (g/L, median, [IQR]) 134 [122–146] 131 [119–143] <0.001 Hematocrit (%, median, [IQR]) 39.7 [36.2–43.1] 39.1 [35.5–42.4] 0.002 MCV (fL, median, [IQR]) 89.1 [86.4–91.7] 89.6 [86.7–92.4] 0.003 MCH (pg, median, [IQR]) 30.6 [29.5–31.6] 30.4 [29.3–31.3] 0.002 MCHC (g/L, median, [IQR]) 343 [335–351] 337 [329–346] <0.001 RDW-CV (Median, [IQR]) 12.2 [11.9–12.8] 12.5 [12.0–13.2] <0.001 RDW-SD (fL, median, [IQR]) 39.5 [37.8–41.8] 40.9 [38.8–43.2] <0.001 PDW (fL, median, [IQR]) 12.0 [10.8–13.6] 12.3 [11.0–13.9] 0.021 Alanine aminotransferase (U/L, 25 [18–38] 24 [16–36] 0.019 median, [IQR]) Aspartate aminotransferase (U/L, 27 [21–36] 25 [19–35] <0.001 median, [IQR]) Ying Luo et al. [7] Total Protein (g/L, mean) 69.3 ± 5.6 68.5 ± 6.4 0.003 Globulin (g/L, median, [IQR]) 32.4 ± 4.4 31.8 ± 4.8 <0.001 Indirect Bilirubin (µmol/L, 5.5 [4.2–7.3] 4.9 [3.8–6.9] <0.001 median, [IQR]) GGT (U/L, median, [IQR]) 30 [21–48] 35 [21–54] 0.003 Alkaline Phosphatase (U/L, 65 [56–78] 75 [63–96] <0.001 median, [IQR]) LDH (U/L, median, [IQR]) 260 [217–327] 235 [196–298] <0.001 Triglyceride (mmol/L, median, [IQR]) 1.75 ± 088 1.63 ± 0.84 <0.001 HDL-C (mmol/L, median, [IQR]) 0.99 ± 0.19 0.97 ± 0.22 0.002 LDL-C (mmol/L, median, [IQR]) 2.45 ± 0.55 2.41 ± 0.68 0.004 Creatinine (µmol/L, median, [IQR]) 72 [61–87] 69 [59–82] <0.001 Urea (mmol/L, median, [IQR]) 5.89 ± 3.84 5.54 ± 3.41 0.001 Uric acid (µmol/L, median, [IQR]) 253 [206–313] 260 [219–304] 0.031 Calcium (mmol/L, median, [IQR]) 2.14 ± 0.11 2.17 ± 0.11 <0.001 Magnesium (mmol/L, median, [IQR]) 0.87 ± 0.07 0.86 ± 0.09 0.001 Chlorine (mmol/L, median, [IQR]) 100.4 ± 4.2 101.4 ± 3.7 <0.001 Potassium (mmol/L, median, [IQR]) 4.21 ± 0.42 4.15 ± 0.40 <0.001 Sodium (mmol/L, median, [IQR]) 139.7 ±3.9 139.1 ± 3.4 <0.001 Phosphate (mmol/L, median, [IQR]) 1.04 ± 0.26 1.05 ± 0.20 0.002 HCO3 (mmol/L, median, [IQR]) 24.5 ± 2.9 24.0 ± 3.1 <0.001 Hypersensitive CRP (mg/L, 20.0 [5.8–45.8] 15.7 [4.8–40.1] 0.024 median, [IQR]) ESR (mm/h, median, [IQR]) 35 [24–47] 27 [17–40] <0.001 Prothrombin time (s, mean) 14.06 ± 1.09 14.09 ± 1.83 <0.001 APTT (s, mean) 39.9 ± 4.5 39.6 ± 5.0 0.02 Thrombin time (s, mean) 16.9 ± 1.4 16.6 ± 2.0 <0.001 Prothrombin activity (%, mean) 91 ± 11 92 ± 14 <0.001 Fibrinogen (g/L, mean) 4.71 ± 1.08 4.27 ± 1.18 <0.001 D-Dimer (mg/L, median, [IQR]) 1.24 [0.65–2.75] 1.72 [0.85–3.30] <0.001 Viruses 2021, 13, 452 8 of 14

Table 3. Cont.

References Signiﬁcant Laboratory Findings COVID-19 (%) Inﬂuenza (%) p-Value < 0.05 Monocyte (×109 /L, median, [IQR]) 0.36 [0.28–0.48] 0.55 [0.4–0.71] 0 Monocyte (%, median, [IQR]) 7.60 [6.20–9.95] 9.0 [7.20–11.40] 0 Neutrophil (×109 /L, median, [IQR]) 2.93 [2.26–3.79] 4.26 [3.00–5.74] 0 Jiangnan Chen et al. [8] Neutrophil (%, mean) 64.50 ± 11.64 68.42 ± 14.69 0.011 Lymphocyte (%, mean) 26.30 ± 10.52 21.07 ± 12.85 0 Eosinophil (%, median, [IQR]) 0.60 [0.30–1.15] 0.40 [0.10–1.10] 0.038 Basophil (%, median, [IQR]) 0.20 [0.10–0.30] 0.10 [0.10–0.30] 0.001 Elevated lymphocyte 0 5 <0.01 Abnormal Urinary test 32.11 21.67 <0.05 Jiajia Qu et al. [9] Urine protein positive 16.26 8.33 <0.05 Elevated procalcitonin 40.83 10.98 <0.01 Elevated white blood cells 75 26.83 <0.01 Leukocytosis > 9.5 × 109 /L 16.1 30.4 0.003 Neutrophilia > 75% 32.2 50.4 0.001 Jianguo Zhang et al. [10] Lymphocytopenia < 20% 46.9 68.7 <0.001 Creatine Kinase > 25 U/L 11.8 3.5 0.013 White Blood cell count (G/L, median, 5.88 [4.41–7.68] 6.72 [5.15–9.42] 0.01 [IQR]) Neutrophil (G/L, median, [IQR]) 4.11 [2.99–5.65] 5.06 [3.43–7.25] 0.02 Platelets (G/L, median, [IQR]) 179 [145–225] 199 [168–239] 0.04 Helene Faury et al. [11] Sodium (U/L, median, [IQR])) 137 [135–139] 138 [136–140] 0.006 Troponin (ng/L, median, [IQR]) 9.2 [6.5–22.4] 34.4 [8.8–72.2] 0.007 Albumin (g/L, median, [IQR]) 30 [27–33] 37 [33–39] 0.04 Aspartate aminotransferase (U/L, 45 [34–76] 34 [29–49] 0.02 median, [IQR]) LDH (U/L, median, [IQR]) 397 [305–544] 298 [248–383] 0.04

PaO2/FiO2 (Median, mm Hg) 198.5 107 <0.001 Aspartate transaminase (U/L) 25.5 70 <0.001 LDH (U/L) 483 767 <0.001 Xiao Tang et al. [13] Troponin I (ng/mL) 0.03 0.14 <0.001 CD3+ (Median, cells/mL) 193 303 0.007 CD4+/CD3+ (Median, cells/mL) 97 185 <0.001 White blood cells at admission 7240 9035 0.007 (median, [IQR]) [5430–11,820] [6590–14,900] Natalie L. Cobb et al. [14] Neutrophil at admission 7210 5405 [3880–9580] 0.02 (median, [IQR]) [4990–11,890] Early sputum cultures (positive) 27.2 72.2 0.005 Leukocytes count ×109 /L 5.1 (4.0–6.3) 6.7 (5.4–10.9) 0.002 (Median, [IQR]) Raija Auvinen et al. [18] Leukocytosis 11 39 0.019 Thrombocytopenia < 150 × 109 /L 39 12 0.019 Alanine aminotransferase (U/L, [IQR]) 42 [19–127] 23 [12–123] 0.011 Neutrophil (×109 cells/L, 3.57 [2.72–4.92] 4.75 [3.15–7.00] 0.037 Zhilan Yin et al. [19] median, [IQR]) Procalcitonin (ng/mL, median, [IQR]) 0.04 [0.03–0.09] 0.11 [0.09–0.37] 0.002 Viruses 2021, 13, 452 9 of 14

Table 3. Cont.

References Significant Laboratory Findings COVID-19 (%) Influenza (%) p-Value < 0.05 White blood cells (×109 cells/L, mean) 4.87 ± 2.04 7.59 ± 5.12 0.026 Leukocytosis 3 32 0.002 Neutrophil (×109 /L, mean) 3.16 ± 1.73 6.20 ± 4.84 0.009 Lymphocyte (×109 /L, mean) 1.19 ± 0.59 0.88 ± 0.52 0.049 Anemia 0 41 <0.001 Yi-Hua Lin et al. [20] 55.3 CRP (mg/L, median, [IQR])) 9.56 [3.82–22.42] 0.001 [33.97–102.77] Procalcitonin (ng/L, median, [IQR]) 0.05 [0.05–0.06] 0.25 [0.08–2.28] <0.001 Urea Nitrogen (mmol/L, mean) 3.76 ± 1.37 6.36 ± 3.30 0.002 158.0 243.5 LDH (U/L, median, [IQR])) <0.001 [142.0–196.0] [198.3–328.8] PaO2/FiO2 < 200 mm Hg 4 22 0.022 Jaehee Lee et al. [21] White blood cell (Median, cells/uL) 7470 2680 0.027 Bold: illness (COVID-19 or influenza) with significant difference. Abbreviations: Leukocytosis: leukocyte count > 10 × 109 /L; CRP: C-reactive protein; LDH: Lactate dehydrogenase; IQR: Interquartile range; MCV: Mean corpuscular volume; MCH: Mean corpuscular hemoglobin; MCHC: Mean corpuscular concentration; RDW-CV: Coefficient variation of red blood cell volume width; RDW_SD: Standard deviation in red cell distribution width; PDW: Platelet distribution width; GGT: γ-glutamyl transpeptidase; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; HCO3: Bicarbonate ion; ESR: Erythrocyte sedimentation rate; APTT: Activated partial thromboplastic time.

Table 4. Significant radiological findings between COVID-19 and influenza adult groups.

References Signiﬁcant Radiological Findings COVID-19 (%) Inﬂuenza (%) p-Value < 0.05 Rounded opacities 37.9 19.1 <0.001 Bronchiolar wall thickening 33.6 13 <0.0001 Air bronchogram 29.9 13 <0.001 Consolidation 26.1 15.7 0.031 Jianguo Zhang et al. [10] Interlobular septal thickening 24.2 13.9 0.029 Crazy paving pattern 22.3 9.6 0.004 Tree-in-bud 13.7 5.2 0.018 GGO with consolidation 25.6 39.1 0.011 Helene Faury et al. [11] Pulmonary nodules 8.8 50.0 0.001 Predominant distribution: – Central 2 6 0.022 – Peripheral 45 20 – Mixed 53 74 Interlobular septal thickening 66 43 0.014 Rounded opacities 35 17 0.048 Nodules 28 71 <0.001 Mengqi Liu et al. [12] Tree-in-bud 9 40 <0.001 Pleural effusion 6 31 <0.001 Pure GGO without nodules 29 11 <0.001 Pure GGO + interlobular septal thickening 21 6 0.042 Rounded opacities without nodules 22 0 0.002 Interlobular septal thickening 45 6 <0.001 without nodules Rounded opacities + interlobular septal 19 3 0.021 thickening + absence of pleural effusion GGO 94.5 45.3 <0.001 Xiao Tang et al. [13] Consolidation 28.8 45.3 0.042 Natalie L Cobb et al. [14] Bilateral opacities 90 52 <0.001 Viruses 2021, 13, 452 10 of 14

Table 4. Cont.

References Significant Radiological Findings COVID-19 (%) Influenza (%) p-Value < 0.05 Lesion Distribution: – Central 7.7 75 0.000 – Peripheral 38.5 3.3 – Diffuse 0 21.7 – Non-specific 53.8 0 Lobe predomination: – Superior lobe 23.1 23.9 0.001 – Inferior lobe 15.4 57.6 – Middle lobe 7.7 7.6 – Balanced predomination 53.8 10.9 Lesion margin: Hao Wang et al. [16] – Clear 46.2 10.9 0.004 – Vague 53.8 89.1 GGO Involvement pattern: – Patchy 38.5 5.4 0.000 – Cluster like 7.7 77.2 – GGO + consolidation opacities 46.2 6.5 – Whole consolidation 7.7 10.9 Lesion Contour: – Shrinking 69.2 1.1 0.000 – Non-shrinking 30.8 98.9 Bronchial wall thickening 0 32.6 0.018 Close to the pleura 69 40 0.005 Mucoid impaction 2 13 0.047 Pleural effusion 0 22 <0.001 Liaoyi Lin et al. [17] Axial distribution: – Inner 6 7 <0.001 – Outer 67 24 – Diffuse 12 36 – Random 15 33 Linear opacities 14 42 0.024 Raija Auvinen et al. [18] GGO/Consolidation 68 21 < 0.001 Vascular enlargement 67 93 0.037 Pleural Thickening 63 90 0.03 Linear opacification 50 90 0.002 Zhilan Yin et al. [19] Crazy-paving sign 30 60 0.021 Pleural effusion 53 13 0.002 Bronchiectasis 30 3 0.012 GGO 71 23 < 0.001 Infiltration 29 68 0.003 Yi-Hua Lin et al. [20] GGO + reticular pattern 63 0 < 0.001 Interlobular septal thickening 71 27 0.001 Bold: illness (COVID-19 or influenza) with significant difference. Abbreviations: GGO: Ground-glass opacity.

4. Discussion The current review compared the clinical features and courses, laboratory data, and radiological findings between COVID-19 patients and those with influenza. We initially found that compared to patients with influenza, COVID-19 patients were more likely to exhibit symptoms such as diarrhea and neurologic symptoms. However, fever, productive cough, vomiting, dyspnea, ocular and otorhinolaryngological symptoms were more observed in the group influenza than the group COVID-19. Some authors concluded by describing the timeline of these viruses that SARS-CoV-2 infection may present with a slow onset compared with the clinical presentations of influenza infection with a longer incubation period [13,15]. Indeed, the differences in the clinical presentation of these two viral infections can be explained, in large part, by the pathophysiological distribution of the entry Viruses 2021, 13, 452 11 of 14

receptors for each virus. Human influenza A virus binds to cell receptors alpha2,6-linked via sialic acid linked glycoproteins. The distribution of sialic acid on cell surfaces is one determinant of host tropism and understanding its expression on human cells and tissues is important for understanding influenza pathogenesis. These receptors were especially expressed on the respiratory tract, from the nasopharynx, trachea to the bronchi, except the alveoli (α2,3-linked sialic acid receptors predominant on alveolar cells) [22]. The short incubation period of influenza infection with the predominant respiratory manifestations (sore throat, sneezing, sputum production, rhonchi on pulmonary auscultation) is well explained by this distribution. On the other hand, Angiotensin-converting enzyme 2 (ACE2) protein, known as the key regulator enzyme of the renin–angiotensin–aldosterone system (RAAS) is the functional receptor of SARS-CoV-2 and its expression and activity will mediate directly the SARS-CoV-2 infection. Regarding the tissue distribution of ACE2 protein, ACE2 is highly expressed on lung alveolar epithelial cells, small intestinal epithelial cells and endothelial cells (including in the central nervous system), but poorly found on the surface of nasopharyngeal cells [23]. When the SARS spike protein binds to the ACE-2 receptor, the complex is proteolytically processed by type 2 transmembrane protease TMPRSS2 leading to cleavage of ACE-2 and activation of the spike [24]. This mechanism is also described in influenza physiopathology. All these findings trigger a longer incubation period of SARS-CoV-2 infection and the observed symptoms in COVID- 19 patients of dyspnea, dry cough, diarrhea, and bilateral crackling sound on pulmonary auscultation, but also neurologic symptoms such as new loss of smell and taste. ACE is also abundantly present in the basal layer of the non-keratinizing squamous epithelium of nasal and oral mucosa [25]. Indeed, most reports have so far linked new loss of taste or smell to neurological symptoms instead of rhinolaryngological symptoms [26]. In any case, in this epidemic context, patients presenting with dysgeusia and/or anosmia may be considered as patients infected with COVID-19, until microbiological confirmation has been obtained. In another study including 217 patients presenting influenza-like illness, we demonstrated that the specificity of the combination of anosmia and dysgeusia reached 91% for a positive SARS-CoV-2 RT-PCR result [27]. Unspecific symptoms such as fever and musculoskeletal symptoms or pain syndrome defined by fatigue, myalgia and/or arthralgia are associated with a cascade of inflammatory mediators and were not directly linked to the distribution of viral receptors. These clinical presentations can be equally described in the two illnesses. Concerning the outcome, both COVID-19 and influenza may be accompanied by ARDS with a high mortality [13]. However, it was clear that mortality and lethality of SARS-CoV-2 infection were much higher than those of the influenza infection in the general population [28]. Comparison of peripheral blood parameters from COVID-19 and influenza patients revealed some differences, especially during the early stages of the disease. Concerning the blood count, NE were significantly more elevated in influenza than COVID-19 patients. Jiangnan Chen et al. conducted their study with a group control [8], the difference about NE is explained by an increase of NE in influenza and a decrease of NE in SARS-CoV-2 infection. The only study which has shown a higher level of lymphocytes in influenza than SARS-CoV-2 infection [20] is not conclusive because the two populations were not comparable (18% received invasive mechanical ventilation (IMV) in the influenza group while none received IMV in the COVID-19 group). Influenza and SARS-CoV-2 infection seems to decrease lymphocytes level with the same intensity. In another short review of COVID-19 hematological manifestations, Slomka et al. showed that the majority of patients (especially those presenting with an ARDS related to SARS-CoV-2) are likely to develop lymphocytopenia. The decreased number of lymphocytes is described in other diseases caused by other coronaviruses primarily infecting the human respiratory tract [29]. This lymphopenia is probably explained by direct infection of lymphocytes and destruction of lymphoid organs caused by the “cytokine storm” and a major release of pro-inflammatory cytokines [30]. On the one hand, it is known that MO and macrophages play central roles in the immune response of humans and in protecting the body from Viruses 2021, 13, 452 12 of 14

influenza infection. They are necessary for the influenza virus to infect lymphocytes and regulate lymphocyte apoptosis by synthesizing and expressing viral neuraminidase [31]. On the other hand, several studies revealed that COVID-19 patients exhibited a significant decrease under lower limit of lymphocyte counts [32]. This indicates that SARS-CoV- 2 consumes many immune cells and inhibits the body’s cellular immune function [33]. Conversely, inflammatory cytokines including serum amyloid A (SAA), CRP, IL-6, IL-10 were significantly higher than normal values [32]. SARS-COV-2 invasion activated T cell- mediated immunity, which resulted in increasing production of inflammatory cytokine [34]. However, inhibition cytokines such as IL-10 can suppress T cell activation conversely. Serum amyloid A and CRP are both of acute-phase proteins in response to inflammatory cytokines related by activated MO/macrophages after infections. High levels of SAA and CRP could reflect the severity of inflammation and result in a “cytokine storm”, during which the inflammation spreads throughout the body via the circulation. This severe inflammatory state secondary to COVID-19 leads to a severe derangement of hemostasis that has been recently described as a state of hypercoagulopathy, defined as increased degradation products such as D-dimer and fibrinogen. In a large American cohort study of 9407 adult COVID-19 patients, the overall in-hospital venous thromboembolism (VTE) was approximately 3%. The authors concluded that key predictors of VTE or mortality included advanced age, increasing CCI, past history of cardiovascular disease, ICU level of care, and elevated level of D-dimer [35]. In addition, several radiologists tried to differentiate COVID- 19 from Influenza and other viral pneumonia on chest CT images [36]. Usually, the common findings on CT for typical influenza pneumonia consist of diffuse or multifocal GGO and small centrilobular nodules [3]. Onigbinde et al. showed that the occurrence of GGO and consolidation in COVID-19 were not markedly different from that of influenza studies. However, they were predominantly located bilaterally within the lower lobes for COVID-19 patients, whereas in the influenza studies, they were more widespread, involving all the lobes [3]. In another study including 122 patients, Mengqi et al. concluded that the COVID- 19 patients were more likely to have rounded opacities and interlobular septal thickening compared with the influenza group, but less likely to have nodules, tree-in-bud sign and pleural effusion with significant difference between the two groups [12]. In our review, complications such as pneumothorax and pneumomediastinum were only described in influenza patients. These respiratory complications remain scarce during the course of COVID-19 and have recently been described in medical literature [3,37,38]. Therefore, these differential pathologic changes may present themselves as distinguishing imaging characteristics during clinical assessments. There are some limitations to our review. First of all, COVID-19 is relatively novel with a limited number of patients and studies. In most articles, the clinical features, laboratory data and CT findings reporting on influenza dated before the onset of COVID 19. Finally, the use of abstracts in selecting full texts for a detailed review could have led to the omission of some articles. Moreover, we only considered PubMed a reference for our bibliographic research and database with accessible free access articles. We also just summarized what we found in recent medical literature and focused on what was statistically significant. Finally, a meta-analysis is recommended to further define the differences and the degree between COVID-19 and influenza.

5. Conclusions This study provided a comprehensive comparison of adult patients in SARS-CoV-2 and influenza infections, regarding comorbidities, clinical and paraclinical features, and outcome. Clinical manifestations of COVID-19 and influenza seem to be similar with some differences: neurologic symptoms and diarrhea were more described in COVID-19, however; vomiting, ocular and otorhinolaryngological symptoms were more observed in influenza infection. Both viruses decreased lymphocytes; NE were significantly more elevated in influenza than COVID-19 patients while elevated transaminases were significantly more elevated in COVID-19 than influenza patients. Radiological findings showed that GGO are usually peripherally located in COVID-19 compared with influenza which Viruses 2021, 13, 452 13 of 14

also had central and random locations. All these findings can help clinicians when deal- ing with cases of influenza-like illnesses during the period of co-circulation of influenza and SARS-CoV-2.

Author Contributions: Conceptualization, M.O., T.K. and S.Z.; methodology, M.O.; software, M.O. and S.Z.; validation, M.O., T.K., N.B., V.G. and S.Z.; formal analysis, M.O.; investigation, M.O. and S.Z.; resources, M.O.; data curation, M.O.; writing—original draft preparation, M.O. and S.Z.; writing—review and editing, M.O., T.K. and S.Z.; visualization, N.B. and V.G.; supervision, T.K., N.B., V.G. and S.Z.; project administration, M.O.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Due to the nature of the study (review), the Ethics & Scien- tific Committee of Nord Franche-Comté Hospital determined that patient consent was required for publication. We make sure to keep patient data confidential and in compliance with the Declaration of Helsinki. Informed Consent Statement: Not applicable. Data Availability Statement: Data available on request due privacy to restrictions. The data pre- sented in this case study are available on request from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References 1. Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [CrossRef] 2. Khan, M.S.; Shahid, I.; Anker, S.D.; Solomon, S.D.; Vardeny, O.; Michos, E.D.; Fonarow, G.C.; Butler, J. Cardiovascular implications of COVID-19 versus influenza infection: A review. BMC Med. 2020, 18, 403. [CrossRef] 3. Onigbinde, S.O.; Ojo, A.S.; Fleary, L.; Hage, R. Chest Computed Tomography Findings in COVID-19 and Influenza: A Narrative Review. Biomed Res. Int. 2020, 2020, 6928368. [CrossRef] 4. WHO. Up to 650 000 People Die of Respiratory Diseases Linked to Seasonal Flu Each Year. Available online: https://www.who. int/news/item/13-12-2017-up-to-650-000-people-die-of-respiratory-diseases-linked-to-seasonal-flu-each-year (accessed on 15 January 2021). 5. Li, P.; Wang, Y.; Peppelenbosch, M.P.; Ma, Z.; Pan, Q. Systematically comparing COVID-19 with 2009 influenza pandemic for hospitalized patients. Int. J. Infect. Dis. 2020, 102, 375–380. [CrossRef] 6. Cates, J.; Lucero-Obusan, C.; Dahl, R.M.; Schirmer, P.; Garg, S.; Oda, G.; Hall, A.J.; Langley, G.; Havers, F.P.; Holodniy, M.; et al. Risk for In-Hospital Complications Associated with COVID-19 and Influenza—Veterans Health Administration, United States, October 1, 2018–May 31, 2020. Morb. Mortal. Wkly. Rep. 2020, 69, 1528–1534. [CrossRef] 7. Luo, Y.; Yuan, X.; Xue, Y.; Mao, L.; Lin, Q.; Tang, G.; Song, H.; Liu, W.; Hou, H.; Wang, F.; et al. Using a diagnostic model based on routine laboratory tests to distinguish patients infected with SARS-CoV-2 from those infected with influenza virus. Int. J. Infect. Dis. 2020, 95, 436–440. [CrossRef] 8. Chen, J.; Pan, Y.; Li, G.; Xu, W.; Zhang, L.; Yuan, S.; Xia, Y.; Lu, P.; Zhang, J. Distinguishing between COVID-19 and influenza during the early stages by measurement of peripheral blood parameters. J. Med. Virol. 2020.[CrossRef][PubMed] 9. Qu, J.; Chang, L.K.; Tang, X.; Du, Y.; Yang, X.; Liu, X.; Han, P.; Xue, Y. Clinical characteristics of COVID-19 and its comparison with influenza pneumonia. Acta Clin. Belg. Int. J. Clin. Lab. Med. 2020, 75, 348–356. [CrossRef][PubMed] 10. Zhang, J.J.; Ding, D.; Huang, X.; Zhang, J.J.; Chen, D.; Fu, P.; Shi, Y.; Xu, W.; Tao, Z. Differentiation of COVID-19 from seasonal influenza: A multicenter comparative study. J. Med. Virol. 2020.[CrossRef][PubMed] 11. Faury, H.; Courboulès, C.; Payen, M.; Jary, A.; Hausfater, P.; Luyt, C.E.; Dres, M.; Pourcher, V.; Abdi, B.; Wirden, M.; et al. Medical Features of COVID-19 and Influenza Infection: A Comparative Study in Paris, France. J. Infect. 2020, 82, E36–E39. [CrossRef] [PubMed] 12. Liu, M.; Zeng, W.; Wen, Y.; Zheng, Y.; Lv, F.; Xiao, K. COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia. Eur. Radiol. 2020, 30, 5463–5469. [CrossRef][PubMed] 13. Tang, X.; Du, R.H.; Wang, R.; Cao, T.Z.; Guan, L.L.; Yang, C.Q.; Zhu, Q.; Hu, M.; Li, X.Y.; Li, Y.; et al. Comparison of Hospitalized Patients with ARDS Caused by COVID-19 and H1N1. Chest 2020, 158, 195–205. [CrossRef] 14. Cobb, N.L.; Sathe, N.A.; Duan, K.I.; Seitz, K.P.; Thau, M.R.; Sung, C.C.; Morrell, E.D.; Mikacenic, C.; Kim, H.N.; Liles, W.C.; et al. Comparison of Clinical Features and Outcomes in Critically Ill Patients Hospitalized with COVID-19 versus Influenza. Ann. Am. Thorac. Soc. 2020.[CrossRef][PubMed] Viruses 2021, 13, 452 14 of 14

15. Zayet, S.; Kadiane-Oussou, N.J.; Lepiller, Q.; Zahra, H.; Royer, P.Y.; Toko, L.; Gendrin, V.; Klopfenstein, T. Clinical features of COVID-19 and influenza: A comparative study on Nord Franche-Comte cluster. Microbes Infect. 2020, 22, 481–488. [CrossRef] 16. Wang, H.; Wei, R.; Rao, G.; Zhu, J.; Song, B. Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia. Eur. Radiol. 2020, 30, 4910–4917. [CrossRef] 17. Lin, L.; Fu, G.; Chen, S.; Tao, J.; Qian, A.; Yang, Y.; Wang, M. CT Manifestations of Coronavirus Disease (COVID-19) Pneumonia and Influenza Virus Pneumonia: A Comparative Study. Am. J. Roentgenol. 2020, 216, 71–79. [CrossRef] 18. Auvinen, R.; Nohynek, H.; Syrjänen, R.; Ollgren, J.; Kerttula, T.; Mäntylä, J.; Ikonen, N.; Loginov, R.; Haveri, A.; Kurkela, S.; et al. Comparison of the clinical characteristics and outcomes of hospitalized adult COVID-19 and influenza patients—A prospective observational study. Infect. Dis. 2020, 53, 111–121. [CrossRef] 19. Yin, Z.; Kang, Z.; Yang, D.; Ding, S.; Luo, H.; Xiao, E. A Comparison of Clinical and Chest CT Findings in Patients with Influenza A (H1N1) Virus Infection and Coronavirus Disease (COVID-19). Am. J. Roentgenol. 2020, 215, 1065–1071. [CrossRef] 20. Lin, Y.H.; Luo, W.; Wu, D.H.; Lu, F.; Hu, S.X.; Yao, X.Y.; Wang, Z.X.; Shi, Y.H. Comparison of clinical, laboratory, and radiological characteristics between SARS-CoV-2 infection and community-acquired pneumonia caused by influenza virus: A cross-sectional retrospective study. Medicine 2020, 99, e23064. [CrossRef] 21. Lee, J.; Lee, Y.H.; Chang, H.H.; Choi, S.H.; Seo, H.; Yoo, S.S.; Lee, S.Y.; Cha, S.I.; Park, J.Y.; Kim, C.H. Comparison of Short-Term Mortality between Mechanically Ventilated Patients with COVID-19 and Influenza in a Setting of Sustainable Healthcare System. J. Infect. 2020, 81, e76–e78. [CrossRef] 22. Kumlin, U.; Olofsson, S.; Dimock, K.; Arnberg, N. Sialic acid tissue distribution and influenza virus tropism. Influenza Other Respir. Viruses 2008, 2, 147–154. [CrossRef][PubMed] 23. Hamming, I.; Timens, W.; Bulthuis, M.; Lely, A.; Navis, G.; van Goor, H. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. 2004, 203, 631–637. [CrossRef] 24. Rabi, F.A.; Al Zoubi, M.S.; Kasasbeh, G.A.; Salameh, D.M.; Al-Nasser, A.D. SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far. Pathogens 2020, 9, 231. [CrossRef] 25. Xu, H.; Zhong, L.; Deng, J.; Peng, J.; Dan, H.; Zeng, X.; Li, T.; Chen, Q. High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa. Int. J. Oral Sci. 2020, 12, 8. [CrossRef] 26. Klopfenstein, T.; Zahra, H.; Kadiane-Oussou, N.J.; Lepiller, Q.; Royer, P.-Y.; Toko, L.; Gendrin, V.; Zayet, S. New loss of smell and taste: Uncommon symptoms in COVID-19 patients on Nord Franche-Comte cluster, France. Int. J. Infect. Dis. 2020, 100, 117–122. [CrossRef] 27. Zayet, S.; Klopfenstein, T.; Mercier, J.; Kadiane-Oussou, N.J.; Lan Cheong Wah, L.; Royer, P.-Y.; Toko, L.; Gendrin, V. Contribution of anosmia and dysgeusia for diagnostic of COVID-19 in outpatients. Infection 2020.[CrossRef] 28. Ji, Y.; Ma, Z.; Peppelenbosch, M.P.; Pan, Q. Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob. Health 2020, 8, e480. [CrossRef] 29. Słomka, A.; Kowalewski, M.; Zekanowska,˙ E. Coronavirus Disease 2019 (COVID–19): A Short Review on Hematological Manifestations. Pathogens 2020, 9, 493. [CrossRef] 30. Fathi, N.; Rezaei, N. Lymphopenia in COVID-19: Therapeutic opportunities. Cell Biol. Int. 2020, 44, 1792–1797. [CrossRef] [PubMed] 31. Roberts, N.J. Diverse and Unexpected Roles of Human Monocytes/Macrophages in the Immune Response to Influenza Virus. Viruses 2020, 12, 379. [CrossRef] 32. Xu, B.; Fan, C.; Wang, A.; Zou, Y.; Yu, Y.; He, C.; Xia, W.; Zhang, J.; Miao, Q. Suppressed T cell-mediated immunity in patients with COVID-19: A clinical retrospective study in Wuhan, China. J. Infect. 2020, 81, e51–e60. [CrossRef] 33. Chowdhury, M.A.; Hossain, N.; Kashem, M.A.; Shahid, M.A.; Alam, A. Immune response in COVID-19: A review. J. Infect. Public Health 2020, 13, 1619–1629. [CrossRef] 34. Bonam, S.R.; Kaveri, S.V.; Sakuntabhai, A.; Gilardin, L.; Bayry, J. Adjunct Immunotherapies for the Management of Severely Ill COVID-19 Patients. Cell Rep. Med. 2020, 1, 100016. Available online: https://www.cell.com/cell-reports-medicine/abstract/S266 6-3791(20)30021-5 (accessed on 2 March 2021). [CrossRef] 35. Cohen, S.L.; Gianos, E.; Barish, M.A.; Chatterjee, S.; Kohn, N.; Lesser, M.; Giannis, D.; Coppa, K.; Hirsch, J.; McGinn, T.; et al. Prevalence and Predictors of Venous Thromboembolism or Mortality in Hospitalized COVID-19 Patients. Thromb. Haemost. 2021. [CrossRef][PubMed] 36. Bai, H.X.; Hsieh, B.; Xiong, Z.; Halsey, K.; Choi, J.W.; Tran, T.M.L.; Pan, I.; Shi, L.-B.; Wang, D.-C.; Mei, J.; et al. Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT. Radiology 2020, 296, E46–E54. [CrossRef] 37. Zayet, S.; Klopfenstein, T.; Mezher, C.; Gendrin, V.; Conrozier, T.; Ben Abdallah, Y. Coronavirus disease 2019 with spontaneous pneumothorax, pneumomediastinum and subcutaneous emphysema, France. New Microbes New Infect. 2020, 38, 100785. [CrossRef] 38. Shim, S.S.; Kim, Y.; Ryu, Y.J. Novel influenza A (H1N1) infection: Chest CT findings from 21 cases in Seoul, Korea. Clin. Radiol. 2011, 66, 118–124. [CrossRef]